Search Results (192)

This study addresses the effects of climate variability and land-use change on groundwater recharge in Major Groundwater Reservoir 406 (MGR 406) in southeastern Poland, a key strategic water resource. It focuses on how regional shifts in precipitation patterns and spatial development influence the volume and distribution of renewable groundwater resources. The analysis integrates meteorological data (1951–2024), groundwater modeling outputs, groundwater-use data, and land cover changes from CORINE datasets (1990–2018). A spatial assessment of hydrogeological conditions was performed using the Groundwater Resources Assessment Index (GRAI), combining drought frequency, recharge conditions, land-use classes, and groundwater extraction levels. Results indicate a long-term increase in annual precipitation alongside more frequent but shorter drought episodes. Urban expansion and land sealing were found to reduce infiltration efficiency, particularly in and around the city of Lublin, where the highest extraction rates were recorded. The assessment identified several zones of high threat to groundwater resources, which have no sufficient legal protection. These findings highlight the need to integrate groundwater management into local spatial planning and land management strategies. The study concludes that balancing water use and recharge potential under evolving climate and land-use pressures are essential to ensuring the sustainability of groundwater resources in MGR 406. Full article

Groundwater quality in arid and semi-arid regions of Morocco is under increasing pressure due to both anthropogenic influences and climatic variability. This study investigates the physicochemical and heavy metal characteristics of groundwater across four Moroccan regions (Tangier-Tetouan-Al Hoceima, Oriental, Souss-Massa, and Marrakech-Safi) known for being argan tree habitats. Thirteen groundwater samples were analyzed for twenty-five parameters, including major ions, nutrients, and trace metals. Elevated levels of ammonium, turbidity, electrical conductivity, and dissolved oxygen were observed in multiple samples, surpassing Moroccan water quality standards and indicating significant quality deterioration. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) detected arsenic concentrations exceeding permissible limits in sample AW11 alongside widespread lead contamination in most samples except AW5 and AW9. Spatial patterns of contamination were characterized using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-means clustering, and GIS-based Inverse Distance Weighted (IDW) interpolation. These multivariate approaches revealed marked spatial heterogeneity and highlighted the dual influence of geogenic processes and anthropogenic activities on groundwater quality. To assess consumption suitability, a Water Quality Index (WQI) and Human Health Risk Assessment were applied. As a result, 31% of samples were rated “Fair” and 69% as “Good”, but with notable non-carcinogenic risks, particularly to children, attributable to nitrate, lead, and arsenic. The findings underscore the urgent need for systematic groundwater monitoring and management strategies to safeguard water resources in Morocco’s vulnerable dryland ecosystems, particularly in regions where groundwater sustains vital socio-ecological species such as argan forests. Full article

In the irrigation districts of Northern China, the flood resources utilization for deep storage irrigation, which is essentially characterized by active excessive irrigation, aims to have the potential to mitigate freshwater shortages, and long-term groundwater overexploitation. It is crucial to detect the effects of irrigation amounts on agricultural yield and the mechanisms under deep storage irrigation. A three-year field experiment (2020–2023) was conducted in the Guanzhong Plain, according to five soil wetting layer depths (RF: 0 cm; W1: control, 120 cm; W2: 140 cm; W3: 160 cm; W4: 180 cm) with soil saturation water content as the irrigation upper limit. Results exhibited that, compared to W1, the W2, W3, and W4 treatments led to the increased plant height, leaf area index, and dry matter accumulation. Meanwhile, the W2, W3, and W4 treatments improved kernel weight increment achieving maximum grain-filling rate (W_max), maximum grain-filling rate (G_max), and average grain-filling rate (G_ave), thereby enhancing the effective spikes (ES) and grain number per spike (GS), and thus increased wheat grain yield (GY). In relative to W1, the W2, W3, and W4 treatments increased the ES, GS, and GY by 11.89–19.81%, 8.61–14.36%, and 8.17–13.62% across the three years. Notably, no significant difference was observed in GS and GY between W3 and W4 treatments, but W4 treatment displayed significant decreases in ES by 3.04%, 3.06%, and 2.98% in the respective years. The application of a structural equation modeling (SEM) revealed that deep storage irrigation improved ES and GS by positively regulating W_max, G_max, and G_ave, thus significantly increasing GY. Overall, this study identified the optimal threshold (W3 treatment) to maximize wheat yields by optimizing both the vegetative growth and grain-filling dynamics. This study provides essential support for the feasibility assessment of deep storage irrigation before flood seasons, which is vital for the balance and coordination of food security and water security. Full article

According to Victor Ernest Shelford’s ‘Law of Tolerance,’ organisms within ecosystems thrive optimally when environmental conditions are favorable. Applying this principle to ecosystems and agro-ecosystems facing water scarcity or environmental challenges can significantly enhance their productivity. In these ecosystems, phytocenosis adjusts its conditions by utilizing water with varying salinity levels. Moreover, establishing optimal drinking water conditions for human populations within an ecosystem can help mitigate future negative succession processes. The purpose of this study is to evaluate the quality of two distinct water sources in the Amudarya district of the Republic of Karakalpakstan, Uzbekistan: collector-drainage water and groundwater at depths of 10 to 25 m. This research is highly relevant in the context of climate change, as improper management of water salinity, particularly in collector-drainage water, may exacerbate soil salinization and degrade drinking water quality. The primary methodology of this study is as follows: The Food and Agriculture Organization of the United Nations (FAO) standard for collector-drainage water is applied, and the water quality index is assessed using the CCME WQI model. The Canadian Council of Ministers of the Environment (CCME) model is adapted to assess groundwater quality using Uzbekistan’s national drinking water quality standards. The results of two years of collected data, i.e., 2021 and 2023, show that the water quality index of collector-drainage water indicates that it has limited potential for use as secondary water for the irrigation of sensitive crops and has been classified as ‘Poor’. As a result, salinity increased by 8.33% by 2023. In contrast, groundwater quality was rated as ‘Fair’ in 2021, showing a slight deterioration by 2023. Moreover, a comparative analysis of CCME WQI values for collector-drainage and groundwater in the region, in conjunction with findings from Ethiopia, India, Iraq, and Turkey, indicates a consistent decline in water quality, primarily due to agriculture and various other anthropogenic pollution sources, underscoring the critical need for sustainable water resource management. This study highlights the need to use organic fertilizers in agriculture to protect drinking water quality, improve crop yields, and promote soil health, while reducing reliance on chemical inputs. Furthermore, adopting WQI models under changing climatic conditions can improve agricultural productivity, enhance groundwater quality, and provide better environmental monitoring systems. Full article

Urbanization always leads to increasing challenges to the groundwater resources in karst regions due to intensive land use, infrastructure development, and the rapid transmission potential of pollutants. This study proposed an improved groundwater vulnerability assessment (GVA) framework by modifying the widely used COP (Concentration of flow, Overlying layers, and Precipitation) model, through the integration of three additional indicators: urban underground rail transportation (UURT), land use and cover (LULC), and karst development (K). Guiyang, a typical urbanized karst city in southwest China, was selected as the case study. The improved COP model, namely the COPK model, showed stronger spatial differentiation and a higher Pearson correlation coefficient ($r$) with nitrate concentrations ($r$ = 0.4388) compared to the original COP model (R = 0.3689), which validates the effectiveness of the newly introduced indicators. However, both R values remained below 0.5, even after model modification, suggesting that intensive human activities play a role in influencing nitrate distribution. The pollution load index (PI) was developed based on seven types of pollution sources, and it was integrated with the COPK vulnerability index using a risk matrix approach, producing a groundwater risk map classified into five levels. Global Moran’s I analysis (0.9171 for COP model and 0.8739 for COPK model) confirmed strong and significant spatial clustering patterns for the two models. The inclusion of UURT and LULC improved the model’s sensitivity to urban-related pressures and enhanced its capacity to detect local risk zones. It is a scalable tool for groundwater risk assessment in urbanized karst areas and offers practical insights for land use planning and sustainable groundwater management. Full article

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = T_a − T_d) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change. Full article

Complex feedback mechanisms and interdependencies exist among the water–energy–food–ecosystems (WEFE) nexus. In water-scarce regions, fluctuations in the supply or demand of any single subsystem can destabilize the others, with water shortages intensifying conflicts among food production, energy consumption, and ecological sustainability. Balancing the synergies and trade-offs within the WEFE system is therefore essential for achieving sustainable development. This study adopts the natural–social water cycle as the core process and develops a coupled simulation model of the WEFE (CSM-WEFE) system, integrating food production, ecological water replenishment, and energy consumption associated with water supply and use. Based on three performance indices—reliability, coupling coordination degree, and equilibrium—a coordinated sustainable development index (CSD) is constructed to quantify the performance of WEFE system under different scenarios. An integrated evaluation framework combining the CSM-WEFE and the CSD index is then proposed to assess the sustainability of WEFE systems. The framework is applied to the Beijing–Tianjin–Hebei (BTH) region, a representative water-scarce area in China. Results reveal that the current balance between water supply and socio-economic demand in the BTH region relies heavily on excessive groundwater extraction and the appropriation of ecological water resources. Pursuing food security goals further exacerbates groundwater overexploitation and ecological degradation, thereby undermining system coordination. In contrast, limiting groundwater use improves ecological conditions but increases regional water scarcity and reduces food self-sufficiency. Even with the full operation of the South-to-North Water Diversion Project (Middle Route), the region still experiences a 16.4% water shortage. By integrating the CSM-WEFE model with the CSD evaluation approach, the proposed framework not only provides a robust tool for assessing WEFE system sustainability but also offers practical guidance for alleviating water shortages, enhancing food security, and improving ecological health in water-scarce regions. Full article

Mediterranean saline wetlands are significant ecological habitats defined by seasonal water availability and various biological communities, forming a unique ecotone that combines traits of both freshwater and marine environments. Moreover, they are regarded as notable natural and economic resources. Since the sustainable management of protected wetlands necessitates a multidisciplinary approach, the purpose of this study is to provide a comprehensive picture of the hydrological, hydrochemical, and ecological dynamics of a man-made groundwater dependent ecosystem (GDE) by combining remote sensing, hydrochemical data, geostatistical tools, and ecological indicators. The study area, called “Le Soglitelle”, is located in the Campania plain (Italy), which is close to the Domitian shoreline, covering a surface of 100 ha. The Normalized Difference Water Index (NDWI), a remote sensing-derived index sensitive to surface water presence, from Sentinel-2 was used to detect changes in the percentage of the wetland inundated area over time. Water samples were collected in four campaigns, and hydrochemical indexes were used to investigate the major hydrochemical seasonal processes occurring in the area. Geostatistical tools, such as principal component analysis (PCA) and independent component analysis (ICA), were used to identify the main hydrochemical processes. Moreover, faunal monitoring using waders was employed as an ecological indicator. Seasonal variation in the inundation area ranged from nearly 0% in summer to over 50% in winter, consistent with the severe climatic oscillations indicated by SPEI values. PCA and ICA explained over 78% of the total hydrochemical variability, confirming that the area’s geochemistry is mainly characterized by the saltwater sourced from the artesian wells that feed the wetland. The concentration of the major ions is regulated by two contrasting processes: evapoconcentration in summer and dilution and water mixing (between canals and ponds water) in winter. Cl⁻/Br⁻ molar ratio results corroborated this double seasonal trend. The base exchange index highlighted a salinization pathway for the wetland. Bird monitoring exhibited consistency with hydrochemical monitoring, as the seasonal distribution clearly reflects the dual behaviour of this area, which in turn augmented the biodiversity in this GDE. The integration of remote sensing data, multivariate geostatistical analysis, geochemical tools, and faunal indicators represents a novel interdisciplinary framework for assessing GDE seasonal dynamics, offering practical insights for wetland monitoring and management. Full article

Groundwater constitutes the primary source of liquid freshwater on Earth and is essential for ecosystems, agriculture, and human consumption. However, rising demand, urbanization, and climate change have intensified groundwater depletion, particularly in semi-arid regions. Therefore, assessing groundwater recharge zones is essential for sustainable water resource management in vulnerable areas such as Dera Ismail Khan, Pakistan. This study aims to delineate groundwater potential zones (GWPZs), using an integrated approach combining the Geographic Information System (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). Twelve factors were identified in a study conducted using GIS-based AHP to determine the groundwater recharge zones in the region. These include land use/land cover (LULC), rainfall, drainage density, soil type, slope, road density, water table depth, and remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Moisture Stress Index (MSI), Worldview Water Index (WVWI), and Land Surface Temperature (LST). The results show that 17.52% and 2.03% of the area have “good” and “very good” potential for groundwater recharge, respectively, while 48.63% of the area has “moderate” potential. Furthermore, gentle slopes (0–2.471°), high drainage density, shallow water depths (20–94 m), and densely vegetated areas (with a high NDVI) are considered important influencing factors for groundwater recharge. Conversely, areas with steep slopes, high temperatures, and dense built-up areas showed “poor” potential for recharge. This approach demonstrates the effectiveness of integrating advanced remote sensing indices with the AHP model in a semi-arid context, validated through high-accuracy field data (Kappa = 0.93). This methodology offers a cost-effective decision support tool for sustainable groundwater planning in similar environments. Full article

This paper investigated sectoral water use and trends in the Mzimvubu to Tsitsikamma Water Management Area 7 (WMA7). The investigation considered the Water Authorisation and Registration Management System (WARMS) database and field surveys as a source of water use information. The study was able to successfully make use of time series statistical analysis to show water use trends for identified priority sectors over a 5-year period by sourcing historical water use data of the study area. Further, the groundwater stress index and streamflow impact were applied to assess water use impacts on the surface and groundwater. The WARMS database and field survey results identified major sectoral water users such as agriculture (irrigation), municipal water services, dam storage, afforestation, power generation, recreation, mining, and industries. Study findings revealed that the agricultural sector is a major water user, with an estimated 60% of the total water requirement over a 5-year period (2018 to 2022). The application of the groundwater stress index revealed that the majority of the Quaternary catchments have surplus groundwater available. The application of streamflow impact revealed that the majority of catchments have low flow or no flow. The rise of water use clearly indicates a lack of water use compliance and enforcement. An increase in total water use could put water resources under stress, including an impact on the aquatic ecosystem, reduced water quality, and economic and social consequences. Therefore, the study recommends that a follow-up on compliance of surface water and groundwater use licenses be regularly conducted. Full article

Ensuring water resources for livestock production in Kazakhstan presents a multifaceted challenge. Pastoral systems in Southern Kazakhstan are facing a critical groundwater shortage, with 56.5% of pastures currently unused due to limited water access, jeopardizing around 2 million head of livestock and the region’s food security. This study presents the first comprehensive groundwater assessment in over 40 years, integrating hydrochemical analysis (55 samples) and field surveys conducted in the Almaty and Zhetysu regions. Key findings include: the total water demand for livestock is estimated at 53,735 thousand m³/year, with approximately 40% of samples exceeding WHO guidelines for total mineralization. It was determined that 45% of exploitable groundwater reserves in the Almaty region and 15–17% in the Zhetysu region are suitable for irrigation. This study also provides updated hydrogeological data, identifying three priority aquifer systems. A novel Groundwater Sustainability Index for pastoral zones of Central Asia is introduced, demonstrating that strategic aquifer development could expand watered pastureland by 30–40%. These findings directly inform Kazakhstan’s Agricultural Development Plan through 2030 and provide a replicable framework for sustainable water management in arid regions. With 69,836 rural residents currently lacking access to safe water, our results underscore the urgent need for infrastructure investment to meet SDG 6 targets (ensure availability and sustainable management of water and sanitation for all). Full article

Overextraction of groundwater, as well as rapidly changing land use patterns, climatic change, and anthropogenic activities, in the densely populated Melur of Tamil Nadu state in India, has led to aquifer degradation. This study maps the groundwater potential (GWPZ) by evaluating 678 km² of this region in the Analytical Hierarchy Processes (AHP) and by using remote sensing and GIS tools as part of SDG 6 for the sustainable management of drinking, irrigation, and industrial uses for future generations. Data information layers, such as aquifer (a), topography (t), lineaments (l), land-use/land-cover (LuLc), soil (s), rainfall (r), and drainage (d) characteristics, separated the study area between poor and excellent groundwater potential zones with 361 km² or 53% of the study area remaining as low GWP and the prospective excellent groundwater potential zone covering only 9 km² (1.3% of total area). The integrated approach of the GWPZ and Water Quality Index (WQI) can effectively identify different zones based on their suitability for extraction and consumption for better understanding. This study also evaluates the performance of three machine learning models, such as Random Forest (RF), Gradient Boosting, and Support Vector Machine (SVM), based on a classification method using the same layers that govern the groundwater potential. The results indicate that both the RF model and Gradient Boosting achieved 100% accuracy, while SVM had a lower accuracy of 50%. Performance metrics such as precision, recall, and F1-score were analyzed to assess classification effectiveness. The findings highlight the importance of model selection, dataset size, and feature importance in achieving optimal classification performance. Results of this study highlight that the aquifer system of Melur has a low groundwater reserve, and it requires adequate water resource management strategies such as artificial recharge, pumping restriction, and implementation of groundwater tariffs for sustainability. Full article

The interplay between terrestrial water storage and vegetation dynamics in arid regions is critical for understanding ecohydrological responses to climate change and human activities. This study examines the coupling between total water storage anomaly (TWSA) and vegetation greenness changes in the Hexi Corridor, an arid region in northwestern China consisting of three inland river basins—Shule, Heihe, and Shiyang—from 2002 to 2022. Utilizing TWSA data from GRACE/GRACE-FO satellites and MODIS Enhanced Vegetation Index (EVI) data, we applied a trend analysis and partial correlation statistical techniques to assess spatiotemporal patterns and their drivers across varying aridity gradients and land cover types. The results reveal a significant decline in TWSA across the Hexi Corridor (−0.10 cm/year, p < 0.01), despite a modest increase in precipitation (1.69 mm/year, p = 0.114). The spatial analysis shows that TWSA deficits are most pronounced in the northern Shiyang Basin (−600 to −300 cm cumulative TWSA), while the southern Qilian Mountain regions exhibit accumulation (0 to 800 cm). Vegetation greening is strongest in irrigated croplands, particularly in arid and hyper-arid regions of the study area. The partial correlation analysis highlights distinct drivers: in the wetter semi-humid and semi-arid regions, precipitation plays a dominant role in driving TWSA trends. Such a rainfall dominance gives way to temperature- and human-dominated vegetation greening in the arid and hyper-arid regions. The decoupling of TWSA and precipitation highlights the importance of human irrigation activities and the warming-induced atmospheric water demand in co-driving the TWSA dynamics in arid regions. These findings suggest that while irrigation expansion cause satellite-observed greening, it exacerbates water stress through increased evapotranspiration and groundwater depletion, particularly in most water-limited arid zones. This study reveals the complex ecohydrological dynamics in drylands, emphasizing the need for a holistic view of dryland greening in the context of global warming, the escalating human demand of freshwater resources, and the efforts in achieving sustainable development. Full article

A hierarchical fuzzy inference system (FIS) integrated with the DRASTIC model is applied in this study to enhance the assessment of shallow groundwater vulnerability in southeast Hungary, a region characterized by extensive agriculture and industrial growth. Traditional groundwater vulnerability models often struggle with parameter imprecision and uncertainty, affecting their reliability. To address these limitations, fuzzy logic was incorporated to refine the classification of vulnerability zones. The hierarchical FIS incorporates the seven DRASTIC parameters: depth to the water table, net recharge, aquifer media, soil media, topography, vadose zone impact, and hydraulic conductivity, assigning flexible ratings through fuzzy membership functions. The model classifies the fuzzy groundwater vulnerability index (FGWVI) into low, moderate, and high categories, revealing that 63.9% of the study area is highly susceptible to contamination, particularly in regions with shallow water tables and sandy soils. Validation was conducted using nitrate (NO₃⁻) concentrations and electrical conductivity (EC) measurements from 46 agricultural wells to assess the correlation between predicted vulnerability zones and actual groundwater quality indicators. The correlation analysis revealed a moderately strong positive relationship between FGWVI and both NO₃⁻ (R² = 0.4785) and EC (R² = 0.528), supporting the model’s ability to identify high-risk contamination zones. This study highlights the effectiveness of the fuzzy-enhanced DRASTIC model in evaluating aquifer vulnerability and provides crucial insights to assist policymakers in identifying pollution sources and developing strategies to mitigate groundwater contamination, thereby alleviating the stress on this critical resource. Full article

Groundwater is crucial for domestic, agricultural, and ecological uses, particularly in the lower reaches of arid basins, where its quality often limits availability. A total of 26 phreatic groundwater samples were collected from a typical endorheic watershed on the Tibetan Plateau to assess the hydrochemical characteristics of phreatic groundwater in the lower reaches of arid inland watersheds. The hydrochemical characteristics, quality, and formation mechanisms of groundwater were analyzed using the Entropy-Weight Water Quality Index (EWQI), irrigation water quality indexes (such as sodium adsorption ratio, soluble sodium percentage, and permeability index), hydrochemical diagrams, and correlation analysis. The findings indicate that phreatic groundwater in the lower reaches is slightly alkaline, with a substantial TDS variation from 252.58 to 1810.41 mg/L. Groundwater is predominantly characterized by fresh hydrochemical facies of HCO₃-Ca and HCO₃-Na types, with a few saline Cl-Na types present. The concentrations of NO₃⁻, NO₂⁻ and NH₄⁺, in groundwater range from 0.32 to 100.00 mg/L, 0.00 to 0.48 mg/L, and 0.00 to 0.20 mg/L, respectively, and 3.59%, 26.92%, and 7.69% of the samples exceeding the permissible drinking limits recommended by Chinese guideline and World Health Organization. Groundwater is classified as fresh at 80.8% of sampling sites and brackish at 19.2%. Approximately 96.2% of the sampled groundwaters is rated as excellent to medium quality according to EWQI assessments, suitable for domestic use, while 3.8% is of extremely poor quality and should be avoided for direct consumption. Groundwater from all sampling sites is suitable for agricultural irrigation and does not pose permeability hazards to the soil. Most groundwaters are suitable for long-term irrigation in terms of sodium hazards, with only 3.8% and 7.7% of samples falling into the “Permissible to Doubtful” and “Doubtful to Unsuitable” categories, respectively. Salinity poses the primary threat in long-term irrigation, with 38.5%, 53.8%, and 7.7% of sampled groundwaters exhibiting moderate, high, and very high salinity risks, respectively. Groundwater chemistry is primarily governed by water-rock interaction and evaporation, with additional impacts from agricultural inputs of nitrogen contaminants and chemicals. Agricultural practices contribute to elevated groundwater salinity in the study area, while natural evaporation drives salinity accumulation in the lower parts. In managing and utilizing groundwater resources in the study area and similar arid regions globally, attention should be paid to salinity caused by agricultural activities and natural evaporation, as well as nitrogen pollution from farming. Full article